Device, configured to operate a machine learning system based on predefinable rollout

ABSTRACT

A device for operating a machine learning system. The machine learning system is assigned a predefinable rollout, which characterizes a sequence in which each of the layers ascertains an intermediate variable. When assigning the rollout, each connection or each layer is assigned a control variable, which characterizes whether the intermediate variable of each of the subsequent connected layers is ascertained according to the sequence or regardless of the sequence. A calculation of an output variable of the machine learning system as a function of an input variable of the machine learning system is controlled as a function of the predefinable rollout. Also described is a method for operating the machine learning system.

FIELD OF THE INVENTION

The present invention relates to a device which is configured to operatea machine learning system. The present invention also relates to amethod and to a computer program for operating the machine learningsystem.

BACKGROUND INFORMATION

The non-prepublished patent specification DE 10 2018 200 724.1 describesa method for efficiently ascertaining output signals of a sequence ofoutput signals from a sequence of input signals with the aid of asequence of layers of a neuronal network.

The input signals of the sequence of input signals are fed successivelyto the neuronal network in a sequence of discrete time steps and signalsof a layer of the sequence of layers present in each case in theneuronal network are further propagated at the discrete time steps.

The publication “The Streaming Rollout of Deep Networks—Towards FullyModel-Parallel Execution” by the authors Volker Fischer, Jan Köhler,Thomas Pfeil at www.arxiv.org with the publication numberarXiv:1806.04965 shows, among others things, mathematical proof that acomplete, parallelized calculation of nodes of a graph for arbitrarystructures of the graph is possible.

SUMMARY

The propagation of signals by a graph, in particular, by a deep neuronalnetwork, is up to now calculated sequentially. This means that the nodesof the graph, in particular, the layers of the deep neuronal network,ascertain an output variable in sequential succession as a function ofan input variable. This results in a sequential dependency of the nodes,since the nodes are only able to ascertain their output variable oncethe respective previous node has ascertained its output variable. Inthis case, the nodes, in particular, layers of the deep neuronalnetwork, must wait until the output variable of the previous node hasarrived at the respective subsequent node. This results in graphs, inparticular, deep neuronal networks, that are slow.

In contrast to the related art, the present invention has the advantagethat it enables the calculations of the nodes to be specificallycontrolled, so that a lesser dependency to no sequential dependency ofthe nodes occurs.

In a first aspect of the present invention, a method is introduced foroperating a machine learning system, in particular, for controlling acalculation of the machine learning system. The machine learning systemincludes a plurality of layers, which are connected with the aid ofconnections. The machine learning system is assigned a predefinablerollout, which characterizes a sequence, according to which each of theseries ascertains an intermediate variable. When assigning the rollout,each connection and/or each layer is assigned a control variable, whichcharacterizes whether the intermediate variable of each of thesubsequent connected layers is ascertained according to the sequence orregardless of the sequence. A calculation of an output variable of themachine learning system as a function of an input variable of themachine learning system is controlled as a function of the predefinablerollout.

Regardless of the sequence is understood below to mean that thecalculations of the intermediate variables of the layers take placedecoupled from the sequence. The sequence may define a succession,according to which each of the layers ascertains the output variables,for example, once a previous layer has ascertained its output variable.

The advantageous effect in this case is that the decoupling means thatthe individual layers may be calculated in a parallelized manner. As aresult, the machine learning system is able to more quickly ascertainthe output variable as a function of the input variable. Furthermore,the calculation time of a complete parallelization of the layers is afunction only of the calculation time of the slowest layer and no longera function of the sum of the calculation times of the individual layers,as is the case with the sequential calculation of the machine learningsystem.

It is provided that when controlling the calculation of the machinelearning system step-wise, in particular, in succession, each of thelayers, in particular, in each case at a predefinable point in time of asequence of points in time, ascertains the intermediate variableaccording to the sequence of the rollout. Those layers that ascertaintheir intermediate variables regardless of the sequence, each ascertaintheir intermediate variables, in each case at each step, in particular,at the respective predefinable points in time.

The advantage in this case is that shorter response times of the machinelearning system may be achieved.

It is further provided that the machine learning system includes atleast one skip connection, which connects a first layer to a secondlayer and the first layer and the second layer are also directlyconnected with the aid of at least two connections.

The advantage of this is that with the skip connection, a higherresponse frequency may be achieved by the skipping of a plurality oflayers and of the decoupling of these layers from the sequence.

It is further provided that the machine learning system includes atleast one recurrent connection.

The advantage is that the decoupling of the layers having recurrentconnections allows for an arbitrarily large virtual memory for recurrentconnections.

It is further provided that those layers that ascertain theirintermediate values regardless of the sequence, ascertain theirintermediate variables as a function of a chronologically precedingintermediate variable, in particular, of a chronologically precedingcalculation step, of the preceding layer. Those layers that ascertaintheir intermediate variable in sequence, ascertain their intermediatevariable as a function of a chronologically instantaneous intermediatevariable, in particular, of an instantaneous calculation step, of thepreceding layer.

It is further provided that the machine learning system does not includea closed path.

The advantage in this case is that by avoiding a closed path, thecalculations of the layers may be completely parallelized. A closed pathis understood to mean that the beginning and the end of the path, whichis defined by connections of the machine learning system, are connectedto one another.

It is further provided that the intermediate variables of those layersthat ascertain their intermediate variable regardless of the sequence,are each ascertained in parallel. Furthermore, the ascertainment inparallel of the intermediate variables may be carried out on processingcores connected in parallel.

It is further provided that the intermediate variables of those layersthat ascertain their intermediate variable regardless of the sequence,are ascertained asynchronously.

It is further provided that when the machine learning system is providedan input variable for the first time, it is checked after each step,when ascertaining step by step according to the sequence of the outputvariable of the machine learning system, whether the intermediatevariables of the previous layer, which are required for those layersthat ascertain their intermediate variables regardless of the sequence,have already been ascertained.

This yields the advantage that it may be checked whether, during thestart-up phase, intermediate variables already ascertained by deeperlayers of the machine learning system may be provided as an inputvariable since these intermediate variables could not yet be ascertainedaccording to the sequence at the point in time of the start-up phase ofthe machine learning system.

It is further provided that a plurality of the control variables of thepredefinable rollout characterize that the respective intermediatevariables are ascertained regardless of the sequence.

The advantage in this case is that the more layers that are decoupledfrom the sequence of the calculations of the layers, the moreextensively the calculations of the layers may be parallelized.

It is further provided that when calculating the machine learningsystem, a sequence of input variables is provided to the machinelearning system, in particular an input layer of the machine learningsystem, in direct succession, in each case, at one time step of asequence of time steps, and a plurality of the layers or each layerascertains the respective intermediate variable at each time step as afunction of an input variable, which is assigned to one each of theinput variables.

The advantage is that multiple provided input variables may be processedsimultaneously in a parallelized manner with the aid of the machinelearning system.

If is further provided that the machine learning system is assigned aplurality of different rollouts. The calculation of the machine learningis controlled in each case as a function of the assigned rollout. Thecontrolled calculations of the machine learning system are compared withat least one predefinable comparison criterion. The predefinable rolloutis selected from the plurality of different rollouts as a function ofthe comparison of the rollouts.

The advantage in this case is that, based on the comparison criterion,it is possible to ascertain a degree of the parallelization of thecalculations of the individual layers as a function of the hardware, inparticular, as a function of the provided processing power of thehardware, and of the specific application. Multiple processor cores maybe efficiently utilized through the parallelization, for example;however, the processor cores are unable, for example, to support anycomplete parallelization of the machine learning system. Accordingly,the degree of parallelization may be ascertained as a function of theprocessor cores by comparing the rollouts.

It is further provided that in one of the rollouts, all connections andlayers are each assigned the same control variable, so that therespective output variables are ascertained regardless of the sequence,in particular, in the subsequent time step.

The advantage is that all layers are decoupled from one another so thatthe calculation may be carried out in a completely parallelized manner.

It is further provided that in one of the rollouts, all connections orlayers are each assigned the same control variable, so that therespective output variables are ascertained regardless of the sequence,in particular, in the subsequent time step.

It is further provided that when assigning the rollout, thoseconnections that connect a first layer to a second layer, and the firstlayer and the second layer are also directly connected with the aid ofat least two connections, are assigned the control variable so that theintermediate variable of the second layer is ascertained regardless ofthe sequence.

It is further provided that the control variables of the rollout areselected at random or as a function of an additional predefinablerollout.

It is further provided that the rollouts are compared with one anotherbased on the predefinable comparison criterion, and the predefinablecomparison criterion as a function of the control of the machinelearning system is ascertained as a function of the respective assignedrollout. The predefinable comparison criterion may include one or aplurality of the following listed comparison criteria:

A first variable, which characterizes a number of time steps required inorder, starting with a first time step at which the input layer isprovided the input variable, to a second time step, at which an outputlayer has ascertained the output variable, the output layer beingconnected to no additional layer.

A second variable, which characterizes how many output variables themachine learning system has ascertained within a predefinable number oftime steps.

A third variable, which characterizes how reliable, in particular, is anaccuracy of the output variable of the machine learning system relativeto the classification accuracy of the machine learning system for therespective rollout.

A fourth variable, which characterizes a period of time after which astart-up phase is completed, in particular, until the reliability of theoutput variable is stable over multiple time steps or calculation steps,or the classification accuracy has reached a maximum value.

A fifth variable, which characterizes how many connections, directly insuccession, include the same control variable.

It is further provided that at least one of the control variables of thepredefinable rollout is changed as a function of a disruption of thecalculation of the machine learning system. A disruption may, forexample, be understood to mean that the machine learning system haserroneously ascertained the output variable or one of the intermediatevariables.

It is further provided that the input variable of the input layer is adetected sensor variable and a control variable is ascertained as afunction of the calculation of the machine learning system.

The control variable may be used to control an actuator of a technicalsystem. The technical system may, for example, be an at leastsemi-autonomous machine, an at least semi-autonomous vehicle, a robot, atool, a factory machine or a flying object, such as a drone.

Alternatively, the input variable may, for example, be ascertained as afunction of detected sensor data and provided to the machine learningsystem. The sensor data may be detected by a sensor such as a camera ofthe technical system, or may be received externally.

In another aspect, a computer program is provided. The computer programis configured to carry out one of the aforementioned methods. Thecomputer program includes instructions, which prompt a computer to carryout one of these aforementioned methods with all its steps, when thecomputer program runs on the computer. Also provided is amachine-readable memory module, on which the computer program is stored.

In another aspect of the present invention, a device for operating amachine learning system is provided, which is configured to operate themachine learning system, the device including a machine-readable memoryelement, on which commands are stored which, when executed by acomputer, ensure that the computer carries out the method that includesthe steps of the first aspect of the present invention.

Exemplary embodiments of the aforementioned aspects are represented inthe appended drawings and are explained in greater detail in thefollowing description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows a representation of a graph, including nodes,which are connected with the aid of edges.

FIG. 2 schematically shows a representation of a calculation of thegraph.

FIG. 3 schematically shows a representation of one specific embodimentof a control of the calculation of the graph.

FIG. 4 schematically shows a representation of one specific embodimentof a use of the graph in an at least semi-autonomous vehicle.

FIG. 5 schematically shows a representation of a device for training amachine learning system.

DETAILED DESCRIPTION

The names of the figures below are based on the publication “TheStreaming Rollout of Deep Networks—Towards Fully Model-ParallelExecution”. It should be noted that a graph may be a machine learningsystem.

FIG. 1 schematically shows a representation of a graph 10. The graphincludes a plurality of nodes a,b,c,d, which are connected with the aidof edges 12.

Edges 12 forward an output variable of a first node (c) to a second noded, in particular, this edge provides the output variable of first node cas an input variable to second node d. Alternatively, the edges mayprocess, for example, weight or transform, the output variable of eachfirst node, and subsequently provide it to the second node. Nodesa,b,c,d ascertain an output variable as a function of their respectivelyprovided input variable, for example, as a function of a non-linearfunction, for this purpose, an activation function of the machinelearning system.

In one exemplary embodiment, graph 10 is a deep neuronal network andnodes a,b,c,d each represent a layer of the deep neuronal network, whichare connected with the aid of the edges.

Graph 10 includes at last one input node 11. Input node 11 receives aninput variable x of graph 10 as its input variable. Input node 11 mayascertain its output variable as a function of provided input variablex. Output variable of input node 11 is forwarded to nodes b,c,d. In theexemplary embodiment in FIG. 1 , node d is an output node, whichascertains as a function of its provided input variables, an outputvariable y, which may be an output variable of graph 10.

Edges 12 of graph 10 each include a control variable S. Control variableS in this exemplary embodiment may assume the value 0 or 1. Value 0characterizes that the output variable, for example, of node a, isprocessed in a subsequent calculation step of a calculation window withthe aid of connected node d according to a provided sequence, in whichthe input variable of graph 10 is propagated by graph 10. This means,node d receives the output variable of node a via an edge and waitsuntil the output variable of node c is provided with the aid of edge 12before node d ascertains its output variable as a function of theseprovided input variables.

If control variable S is assigned value 1, this means that the outputvariable, for example, of node a, is processed with the aid of thesubsequently connected node d in the subsequent calculation step of thecalculation window, and does not have to wait until the node is next inthe provided sequence. This means that node d does not have to waituntil the output variable of node a is propagated along a path, forexample, via node c to node d and arrives at node d. Rather, node d mayuse the output variable of node c and the output variable of node adirectly in the subsequent calculation step in order to ascertain itsoutput variable y. This has the advantageous effect that node d does nothave to wait until this node is provided the input variable with the aidof the edge and is thus decoupled from the provided sequence.

FIG. 2 schematically shows a representation of a control 20 of thecalculation of graph 10. For this purpose, FIG. 2 shows a time axis t,which is assigned multiple points in time t=0, t=1, t=2, t=3, each ofwhich indicates a beginning of a calculation step. In another exemplaryembodiment, graph 10 is provided an input variable x_0, x_1, x_2, x_3 ateach point in time t=0, t=1, t=2, t=3 and is processed with the aid ofgraph 10. Since in one exemplary embodiment of FIG. 2 , an inputvariable is processed at each point in time, in particular, all nodes ofthe graph process an input variable, graph 10 may output an outputvariable y_0, y_1, y_2, y_3 at each point in time t=0, t=1, t=2, t=3.

In the exemplary embodiment according to FIG. 2 , each edge of graph 10is assigned a control variable S. Control variable S has either value 0or value 1. If one of the edges includes control variable S having value0, this edge is marked in FIG. 2 by a solid line. If one of the edgesincludes control variable S having value 1, this edge is marked in FIG.2 by a dashed line. It should be noted that the solid edges provide theoutput variables to each connected node as an input variable only withinone calculation step. In contrast, the dashed edges are able to forwardthe output variable along time axis t, so that this output variable isprovided to the respective node as an input variable at the subsequentcalculation step.

At point in time t=0, graph 10 is provided input variable x_0. The inputnode of graph 10 may process provided input variable x_0 within thiscalculation step, which began at point in time t=0. The fact that thisnode of graph 10 has processed the input variable, is characterized inFIG. 2 in that the node is outlined in black and a counter isincremented within the node, in this example, with value 1. Alladditional nodes of graph 10 each include a counter that has value 0,since these nodes have not yet carried out any processing of theirrespectively provided input variables.

An additional input variable x_1 may be provided graph 10 at immediatelysubsequent point in time t=1. Other input variable x_1 in this case isprocessed again by the input node, as a result of which its counter isincremented and now has value 2. Since node a and node b in thisexemplary embodiment are connected by an edge that includes controlvariable S having value 0, node b waits until it is provided the outputvariable of node a. Once this node has calculated its output variable,this variable is marked in bold and its counter is incremented to value1.

Since node a is connected by an edge to node d, and control value Sequals 1, the output variable of node a is forwarded directly from thefirst calculation step, beginning at point in time t=0, and used againfor the subsequent calculation step at point in time t=1. In this way,node d may ascertain its output variable already at point in time t=1and is also outlined in bold and its counter is set to value 1.

At point in time t=2, node c receives the calculated output variable ofnode b and ascertains its output variable as a function of this outputvariable.

Its counter is subsequently incremented.

At subsequent point in time t=3 of preceding point in time t=2, inputvariable x_0 has been propagated along the entire path of graph 10 andoutput node d outputs associated output variable y_3, which may beassigned to input variable x_0.

Based on the edge that includes control variable S having value 1, itbecomes apparent that the subsequent connected node ascertains itsoutput variable with the aid of this edge, regardless of the sequence,which according to the sequence of the nodes that process in successionthe input variable of graph 10.

In another exemplary embodiment, in which the edge, that connects node ato node b, includes control value S equaling 0, node d would have towait until node c has ascertained its output variable. As a result, itbecomes apparent that by specifically decoupling the node of graph 10,the calculation may be specifically accelerated, since the nodes nolonger have to wait for the output variables of the previous node.

FIG. 3 schematically shows a representation of a method 30 forcalculating graph 10.

Method 30 starts with step 31. In step 31, graph 10 is provided. Infollowing step 32, graph 10 is assigned a predefinable rollout. Whenassigning this rollout, each edge and/or each node is assigned a controlvariable S.

Step 33 follows upon completion of step 32. In step 33, graph 10 isprovided an input variable. Graph 10 then ascertains the output variableof graph 10 as a function of the provided input variable. In this case,the calculation of the output variable of graph 10 is controlled as afunction of the assigned rollout.

This ends method 30. It should be noted, however, that the method foroperating a trained graph, in particular, of a trained machine learningsystem, may also be used when training the graph in order to ascertainoutput variable y as a function of a provided input variable x.

FIG. 4 schematically shows a use of graph 10, which in this exemplaryembodiment is a machine learning system 42 in an at leastsemi-autonomous vehicle 40. In another exemplary embodiment, the atleast semi-autonomous vehicle 40 may be a service robot, assembly robotor stationary manufacturing robot, alternatively an autonomous flyingobject, such as a drone. The at least semi-autonomous vehicle 40 mayinclude a detection unit 41. Detection unit 41 may, for example, be acamera, which detects surroundings of vehicle 40. Detection unit 41 maybe connected to machine learning system 42. Machine learning system 42ascertains an output variable as a function of a provided inputvariable, for example, provided by detection unit 41, and as a functionof a plurality of parameters of machine learning system 42. The outputvariable may be forwarded to an actuator control unit 43. Actuatorcontrol unit 43 controls an actuator as a function of the outputvariable of machine learning system 42, preferably controls thisactuator in such a way that vehicle 40 executes a collision-freemaneuver. The actuator in this exemplary embodiment may be an engine ora braking system of vehicle 40.

Vehicle 40 further includes a processing unit 44 and a machine-readablememory unit 45. A computer program, which includes commands which, whenthe commands are carried out on processing unit 45, result in processingunit 45 carrying out the method for operating machine learning system 42as shown, for example, in FIG. 3 , may be stored on memory element 45.It is also conceivable that a download product or an artificiallygenerated signal, each of which may include the computer program, afterbeing received at a receiver of vehicle 40, prompts processing unit 44to carry out the method for operating a machine learning system.

In one alternative exemplary embodiment, machine learning system 42 maybe used for a building control system. A user behavior is detected withthe aid of a sensor, for example, of a camera or of a motion detector,and the actuator control unit controls a heat pump of a heating unit,for example as a function of the output variable of machine learningsystem 42. Machine learning system 42 may then be configured toascertain which operating mode of the building control system isrequested, based on the detected user behavior.

In another exemplary embodiment, actuator control unit 43 includes arelease system. The release system decides whether an object, forexample, a detected robot or a detected person, has access to an area asa function of the output variable of machine learning system 42. Theactuator, for example, a door opening mechanism, is preferably activatedwith the aid of actuator control unit 43. Actuator control unit 43 ofthe previous exemplary embodiment of the building control system mayalso include this release system.

In one alternative exemplary embodiment, vehicle 40 may be a tool, afactory machine or a manufacturing robot. A material of a workpiece maybe classified with the aid of machine learning system 42. The actuatorin this case may, for example, be a motor that drives a grinding head.

In another specific embodiment, machine learning system 42 is used in ameasuring system, which is not depicted in the figures. The measuringsystem differs from vehicle 40 according to FIG. 4 in that the measuringsystem does not include actuator control unit 43. Instead of forwardingit to actuator control unit 43, the measuring system may store orrepresent the output variable of first machine learning system 42, forexample, with the aid of visual or auditory representations.

It is also conceivable that in a refinement of the measuring system,detection unit 41 detects an image of a human or of an animal body or ofa part thereof. This may take place, for example, with the aid of avisual signal, with the aid of an ultrasonic signal or with the aid of aMRT/CT method. The measuring system in this refinement may includelearning system 42, which is trained in such a way as to output aclassification as a function of the input variable, for example, whichclinical picture is potentially present based on this input variable.

FIG. 5 schematically shows a representation of a device 50 for traininggraph 10, in particular, the machine learning system. Device 50 includesa training module 51 and a module 52 to be trained. This module 52 to betrained contains graph 10. Device 50 for training graph 10, trains graph10 as a function of output variables of graph 10 and preferably withpredefinable training data. For this purpose, the training data includea plurality of detected images, each of which are labeled. During thetraining, parameters of graph 10 stored in a memory 53 are adapted.

What is claimed is:
 1. A device configured to operate a machine learning system, the machine learning system including a plurality of layers, which are connected with the aid of connections, the device comprising: a machine-readable memory element, on which commands are stored which, when executed by a computer, ensure that the computer carries out a method that includes the following steps: assigning to the machine learning system a predefinable rollout, which characterizes a sequence, according to which the layers each ascertain an intermediate variable, when assigning the predefinable rollout, assigning to each connection or each layer a control variable, which characterizes whether the intermediate variable of the respective subsequent connected layers is ascertained according to the sequence or regardless of the sequence, and calculating an output variable of the machine learning system as a function of an input variable of the machine learning system, the calculating being controlled as a function of the predefinable rollout, wherein the control variables of the rollout are selected at random or as a function of an additional predefinable rollout.
 2. The device as recited in claim 1, wherein when controlling the calculation, each of the layers that ascertains according to the sequence ascertains step-wise and in succession the intermediate variable according to the sequence of the rollout, in each case at a predefinable point in time of a sequence of points in time, and those layers that ascertain their intermediate variables regardless of the sequence each ascertain their intermediate variables, in each case at each step at the respective predefinable points in time, and wherein the machine learning system is assigned a plurality of different rollouts, in each case the calculating of the machine learning system being controlled as a function of the assigned rollouts, the controlled calculating of the machine learning system for each of the assigned rollouts being compared with one another based on at least one predefinable comparison criterion, the predefinable rollout being selected as a function of the comparison.
 3. The device as recited in claim 1, wherein the sequence is executed step by step, in each step at least one of the layers ascertaining its output variable according to the sequence.
 4. The device as recited in claim 1, wherein the machine learning system includes at least one skip connection, which connects a first layer to a second layer and the first layer and the second layer are also directly connected with the aid of at least two connections.
 5. The device as recited in claim 4, wherein when assigning the rollout, those connections that connect a first layer with a second layer and the first layer and the second layer are also directly connected with the aid of at least two connections, are assigned the control variable, so that the intermediate variable of the second layer is ascertained regardless of the sequence.
 6. The device as recited in claim 1, wherein the machine learning system includes at least one recurrent connection.
 7. The device as recited in claim 1, wherein those layers that ascertain their intermediate variables regardless of the sequence, ascertain their intermediate variables as a function of a chronologically preceding intermediate variable of a chronologically preceding calculation step of the previous layer, and those layers that ascertain their intermediate variable according to the sequence, ascertain their intermediate variable as a function of a chronologically instantaneous intermediate variable of an instantaneous calculation step of the preceding layer.
 8. The device as recited in claim 1, wherein the machine learning system does not include a closed path.
 9. The device as recited in claim 1, wherein the intermediate variables of those layers, that ascertain their intermediate variable regardless of the sequence, are each ascertained in parallel.
 10. The device as recited in claim 9, wherein the ascertainment in parallel of the intermediate values is carried out on processor cores connected in parallel.
 11. The device as recited in claim 1, wherein the intermediate variables of those layers, that ascertain their intermediate variable regardless of the sequence, are ascertained asynchronously.
 12. The device as recited in claim 1, wherein after the machine learning system is provided an input variable for the first time, it is checked after each step during a step-wise ascertainment according to the sequence of the output variable of the machine learning system, whether those layers that ascertain their intermediate variable regardless of the sequence are each provided an already ascertained intermediate variable of a previous layer.
 13. The device as recited in claim 1, wherein a plurality of the control variables of the predefinable rollout characterize that respective intermediate variables are ascertained regardless of the sequence.
 14. The device as recited in claim 1, wherein during the calculation of the machine learning system, the machine learning system is provided a sequence of input variables of an input layer of the machine learning system, in direct succession, in each case, at a time step of a sequence of time steps, each layer ascertaining at each time step as a function of an input variable, the respective intermediate variable, which in each case is assigned to one of the input variables.
 15. The device as recited in claim 1, wherein in the case of one of the rollouts, all connections and layers are each assigned the same control variable, so that each of the output variables is ascertained regardless of the sequence.
 16. The device as recited in claim 1, wherein in the case of one of the rollouts, all connections or layers are each assigned the same control variable, so that each of the output variables is ascertained regardless of the sequence.
 17. The device as recited in claim 1, wherein the rollouts are compared with one another based on the predefinable comparison criterion, the predefinable criterion as a function of the control of the machine learning system being ascertained as a function of the respectively assigned rollout, the predefinable criterion including: a first variable, which characterizes a number of time steps required in order, starting with a first time step at which the input layer is provided the input variable, to ascertain the output variable up to a second time step, the output layer not being connected to any additional layer.
 18. The device as recited in claim 17, wherein the predefinable criterion includes a variable that characterizes how many output variables the machine learning system ascertains within a predefinable number of time steps.
 19. The device as recited in claim 17, wherein the predefinable criterion includes a variable that characterizes how reliable an accuracy of the output variable of the machine learning system is with the aid of the respective rollout.
 20. The device as recited in claim 17, wherein the predefinable criterion includes a variable that characterizes a period of time after which a start-up phase is completed, or the classification accuracy has reached a maximum value.
 21. The device as recited in claim 17, wherein the predefinable criterion includes a variable that characterizes how many connections in direct succession include the same control variable.
 22. The device as recited in claim 1, wherein the rollouts are also compared with a rollout based on the predefinable comparison criterion, in which all control variables provide the processing of the results according to the sequence.
 23. The device as recited in claim 1, wherein the layers of the machine learning system are in each case a layer of a deep neuronal network.
 24. The device as recited in claim 23, wherein the machine learning system classifies an image sequence.
 25. The device as recited in claim 24, wherein the classification that takes place image element-wise is segmented.
 26. The device as recited in claim 1, wherein the input variable of the input layer is a detected sensor variable and a control variable is ascertained as a function of the calculation of the machine learning system.
 27. The device as recited in claim 1, wherein the device is used for training the machine learning system.
 28. The device as recited in claim 1, wherein the device is used for a real time processing of a video with the aid of the machine learning system.
 29. The device as recited in claim 1, wherein the device is for controlling a calculation of the machine learning system.
 30. A device configured to operate a machine learning system, the machine learning system including a plurality of layers, which are connected with the aid of connections, the device comprising: a machine-readable memory element, on which commands are stored which, when executed by a computer, ensure that the computer carries out a method that includes the following steps: assigning to the machine learning system a predefinable rollout, which characterizes a sequence, according to which the layers each ascertain an intermediate variable, when assigning the predefinable rollout, assigning to each connection or each layer a control variable, which characterizes whether the intermediate variable of the respective subsequent connected layers is ascertained according to the sequence or regardless of the sequence, and calculating an output variable of the machine learning system as a function of an input variable of the machine learning system, the calculating being controlled as a function of the predefinable rollout, wherein at least one of the control variables of the predefinable rollout is changed as a function of a disruption of the calculation of the machine learning system.
 31. The device as recited in claim 30, wherein the disruption includes that the machine learning system has erroneously ascertained the output variable or one of the intermediate variables. 